Describing complex cells in primary visual cortex

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorWestö, Johan
dc.contributor.authorMay, Patrick J.C.
dc.contributor.departmentDepartment of Neuroscience and Biomedical Engineering
dc.contributor.departmentLancaster University
dc.date.accessioned2018-09-04T11:13:46Z
dc.date.available2018-09-04T11:13:46Z
dc.date.issued2018-08-09
dc.description.abstractReceptive field (RF) models are an important tool for deciphering neural responses to sensory stimuli. The two currently popular RF models are multifilter linear-nonlinear (LN) models and context models. Models are, however, never correct, and they rely on assumptions to keep them simple enough to be interpretable. As a consequence, different models describe different stimulus-response mappings, which may or may not be good approximations of real neural behavior. In the current study, we take up two tasks: 1) we introduce new ways to estimate context models with realistic nonlinearities, that is, with logistic and exponential functions, and 2) we evaluate context models and multifilter LN models in terms of how well they describe recorded data from complex cells in cat primary visual cortex. Our results, based on single-spike information and correlation coefficients, indicate that context models outperform corresponding multifilter LN models of equal complexity (measured in terms of number of parameters), with the best increase in performance being achieved by the novel context models. Consequently, our results suggest that the multifilter LN-model framework is suboptimal for describing the behavior of complex cells: the context-model framework is clearly superior while still providing interpretable quantiza-tions of neural behavior. NEW & NOTEWORTHY We used data from complex cells in primary visual cortex to estimate a wide variety of receptive field models from two frameworks that have previously not been compared with each other. The models included traditionally used multifilter linear-nonlinear models and novel variants of context models. Using mutual information and correlation coefficients as performance measures, we showed that context models are superior for describing complex cells and that the novel context models performed the best.en
dc.description.versionPeer revieweden
dc.format.extent17
dc.format.extent703-719
dc.format.mimetypeapplication/pdf
dc.identifier.citationWestö , J & May , P J C 2018 , ' Describing complex cells in primary visual cortex : A comparison of context and multifilter LN models ' , Journal of Neurophysiology , vol. 120 , no. 2 , pp. 703-719 . https://doi.org/10.1152/jn.00916.2017en
dc.identifier.doi10.1152/jn.00916.2017
dc.identifier.issn0022-3077
dc.identifier.otherPURE UUID: 98159d98-ea84-46b8-b3fc-eae2e20157c8
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/98159d98-ea84-46b8-b3fc-eae2e20157c8
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85051854060&partnerID=8YFLogxK
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/27537836/jn.00916.2017.pdf
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/33814
dc.identifier.urnURN:NBN:fi:aalto-201809044934
dc.language.isoenen
dc.relation.ispartofseriesJournal of Neurophysiologyen
dc.relation.ispartofseriesVolume 120, issue 2en
dc.rightsopenAccessen
dc.subject.keywordComplex cell
dc.subject.keywordContext model
dc.subject.keywordLn model
dc.subject.keywordReceptive field
dc.subject.keywordStimulus-response mapping
dc.titleDescribing complex cells in primary visual cortexen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
dc.type.versionpublishedVersion
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